{
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   "execution_count": 1,
   "id": "58b44bda-a73e-4ad3-8b65-af014e1b23e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa010be0-e84e-4c86-a7bd-e4fcb7b545ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jsputils import classes, nnutils, feature_extractor\n",
    "import os\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a35081da-1b98-4a0c-ae24-0ef0b87a1170",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'alexnet-barlow-twins'\n",
    "readout_from = 'relu7'\n",
    "sparse_pos = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2bbf24c-6620-4ecc-a8d2-c2a88f76939b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... loading checkpoint: barlow_alexnet_gn_imagenet_final.pth.tar\n",
      "... state loaded.\n"
     ]
    }
   ],
   "source": [
    "DNN = classes.DNNModel(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fbb147b-dfc6-4a3e-bc92-59a66ea720ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'alexnet-barlow-twins'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DNN.model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88366861-8774-4934-ba84-462ca08708e3",
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    "jupyter": {
     "outputs_hidden": true
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Namespace(device='cuda:0', num_workers=128, log_freq=10, train_epochs=10, batch_size=512, max_lr=0.05, initial_lr=0.001, pct_start=0.3, sparse_pos=False, l1_pos_lambda=0.0001, l1_neg_lambda=0.0001, readout_from='relu7', wandb_repo='DNFFA')\n",
      "BarlowTwins(\n",
      "  (backbone): Sequential(\n",
      "    (0): Sequential(\n",
      "      (0): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2), bias=False)\n",
      "      (1): GroupNorm(32, 96, eps=1e-05, affine=True)\n",
      "      (2): ReLU()\n",
      "      (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    )\n",
      "    (1): Sequential(\n",
      "      (0): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
      "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "      (2): ReLU()\n",
      "      (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    )\n",
      "    (2): Sequential(\n",
      "      (0): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
      "      (2): ReLU()\n",
      "    )\n",
      "    (3): Sequential(\n",
      "      (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (1): GroupNorm(32, 384, eps=1e-05, affine=True)\n",
      "      (2): ReLU()\n",
      "    )\n",
      "    (4): Sequential(\n",
      "      (0): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
      "      (2): ReLU()\n",
      "      (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    )\n",
      "    (5): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  )\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (projector): Sequential(\n",
      "    (0): Linear(in_features=9216, out_features=4096, bias=False)\n",
      "    (1): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=False)\n",
      "    (4): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU()\n",
      "  )\n",
      "  (readout): Linear(in_features=4096, out_features=1000, bias=False)\n",
      ")\n",
      "mdl-alexnet-barlow-twins_from-relu7_mlr-0.05_ilr-0.001_eps-10_sparse-pos-False\n"
     ]
    },
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      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjacob-prince\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.run.save without any arguments is deprecated.Changes to attributes are automatically persisted.\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/dnffa/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/jovyan/.local/share/jupyter/runtime/kernel-3f4cccb7-d262-47fd-82f0-dd8451d42358.json\n",
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      "evaluating for epoch 9...\n"
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    "DNN.train_linear_probe(readout_from = readout_from, sparse_pos = sparse_pos)"
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